Search: “research”

On the Non-decoupling of Supervised Fine-tuning and Reinforcement Learning in Post-training

arXiv:2601.07389v2 Announce Type: replace Abstract: Post-training of large language models routinely interleaves supervised fine-tuning (SFT) with reinforcement learning (RL). These two methods have d

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On the Wasserstein Gradient Flow Interpretation of Drifting Models

DGX agent

arXiv:2605.05118v1 Announce Type: new Abstract: Recently, Deng et al. (2026) proposed Generative Modeling via Drifting (GMD), a novel framework for generative tasks. This note presents an analysis of

researcharxiv-cs-lg

7 May 2026

Online Nonstochastic Prediction: Logarithmic Regret via Predictive Online Least Squares

arXiv:2605.04364v1 Announce Type: new Abstract: We study online prediction for marginally stable, partially observed linear dynamical systems under nonstochastic disturbances. Our objective is to mini

Optimize-at-Capture: Highly-adaptive Exposure Controlling for In-Vehicle Non-contact Heart-rate Monitoring

arXiv:2605.04397v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) holds great promise for continuous heart-rate monitoring of drivers in intelligent vehicles. However, its performance

Pack it in: Packing into Partially Filled Containers Through Contact

arXiv:2602.12095v3 Announce Type: replace Abstract: The automation of warehouse operations is crucial for improving productivity and reducing human exposure to hazardous environments. One operation fr

researcharxiv-cs-ro

PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data

arXiv:2605.04838v1 Announce Type: cross Abstract: The standard constraint-based paradigm for causal discovery with incomplete data -- impute first, test second -- is frequently miscalibrated: any cons

PhysForge: Generating Physics-Grounded 3D Assets for Interactive Virtual World

arXiv:2605.05163v1 Announce Type: new Abstract: Synthesizing physics-grounded 3D assets is a critical bottleneck for interactive virtual worlds and embodied AI. Existing methods predominantly focus on

Physics-Guided Regime Unmixing

arXiv:2605.04247v1 Announce Type: new Abstract: The Linear Mixing Model (LMM) dominates spectral unmixing for its simplicity, but fails under multiple scattering; existing nonlinear models compensate

Physiologically Grounded Driver Behavior Classification: SHAP-Driven Elite Feature Selection and Hybrid Gradient Boosting for Multimodal Physiological Signals

arXiv:2605.05120v1 Announce Type: new Abstract: An interpretable and scalable framework for decoding driving behaviors from multimodal physiological signals is proposed in this study. We utilize multi

Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities

arXiv:2605.04127v1 Announce Type: cross Abstract: Model collapse, the degradation in performance that arises when generative models are trained on the outputs of prior models, is an increasing concern

PRISM: Color-Stratified Point Cloud Sampling

arXiv:2601.06839v2 Announce Type: replace Abstract: We present PRISM, a novel color-guided stratified sampling method for RGB-LiDAR point clouds. Our approach is motivated by the observation that uniq

Probabilistic Classification and Uncertainty Quantification of Sahara Desert Climate Using Feedforward Neural Networks

arXiv:2605.04286v1 Announce Type: new Abstract: Climate classification plays a vital role in agricultural planning, hydrological studies, and climate science. One of the most widely used systems for c

Programmatic Context Augmentation for LLM-based Symbolic Regression

arXiv:2605.03101v1 Announce Type: new Abstract: Symbolic regression (SR), the task of discovering mathematical expressions that best describe a given dataset, remains a fundamental challenge in scient

researcharxiv-cs-ai

Progressive J-Invariant Self-supervised Learning for Low-Dose CT Denoising

arXiv:2601.14180v3 Announce Type: replace Abstract: Self-supervised learning has been increasingly investigated for low-dose computed tomography (LDCT) image denoising, as it alleviates the dependence

QuadBox: Accelerating 3D Gaussian Splatting with Geometry-Aware Boxes

arXiv:2605.04844v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged as an advanced technique for real-time novel view synthesis by representing scene geometry and appearance using

QUIVER: Cost-Aware Adaptive Preference Querying in Surrogate-Assisted Evolutionary Multi-Objective Optimization

arXiv:2605.04267v1 Announce Type: new Abstract: Interactive multi-objective optimization systems face a budget allocation dilemma: one can spend resources on expensive objective evaluations or on elic

RaguTeam at SemEval-2026 Task 8: Meno and Friends in a Judge-Orchestrated LLM Ensemble for Faithful Multi-Turn Response Generation

arXiv:2605.04523v1 Announce Type: new Abstract: We present our winning system for Task~B (generation with reference passages) in SemEval-2026 Task~8: MTRAGEval. Our method is a heterogeneous ensemble

RealLiFe: Real-Time Light Field Reconstruction via Hierarchical Sparse Gradient Descent

arXiv:2307.03017v5 Announce Type: replace Abstract: With the rise of Extended Reality (XR) technology, there is a growing need for real-time light field reconstruction from sparse view inputs. Existin

Reduced-order Neural Modeling with Differentiable Simulation for High-Detail Tactile Perception

arXiv:2605.05053v1 Announce Type: cross Abstract: Tactile perception is key to dexterous manipulation, yet simulating high-resolution elastomer deformation remains computationally prohibitive. Finite

Replay-Based Continual Learning for Physics-Informed Neural Operators

arXiv:2605.04832v1 Announce Type: new Abstract: Neural operators generally demonstrate strong predictive performance on in-distribution (ID) problems. However, a critical limitation of existing method

RetentiveKV: State-Space Memory for Uncertainty-Aware Multimodal KV Cache Eviction

arXiv:2605.04075v1 Announce Type: cross Abstract: Multimodal Large Language Models face severe challenges in computational efficiency and memory consumption due to the substantial expansion of the vis

Reward-Guided Semantic Evolution for Test-time Adaptive Object Detection

arXiv:2605.04531v1 Announce Type: new Abstract: Open-vocabulary object detection with vision-language models (VLMs) such as Grounding DINO suffers from performance degradation under test-time distribu

Right Model, Right Time: Real-Time Cascaded-Fidelity MPC for Bipedal Walking

arXiv:2605.04607v1 Announce Type: new Abstract: This paper presents a multi-phase whole-body model predictive control approach for bipedal walking, combining a detailed whole-body model in the near ho

researcharxiv-cs-ro

Row-stochastic matrices can provably outperform doubly stochastic matrices in decentralized learning

arXiv:2511.19513v2 Announce Type: replace Abstract: Decentralized learning often involves a weighted global loss with heterogeneous node weights lambda. We revisit two natural strategies for incorpora

Same Voice, Different Lab: On the Homogenization of Frontier LLM Personalities

arXiv:2605.02897v1 Announce Type: cross Abstract: LLM assistant personalities play a critical role in user experience and perceived response quality. We present a large-scale experiment of frontier LL

researcharxiv-cs-ai

SAMIC: A Lightweight Semantic-Aware Mamba for Efficient Perceptual Image Compression

arXiv:2605.04560v1 Announce Type: new Abstract: Perceptual image compression focuses on preserving high visual quality under low-bitrate constraints. Most existing approaches to perceptual compression

SCOUT: Active Information Foraging for Long-Text Understanding with Decoupled Epistemic States

arXiv:2605.04496v1 Announce Type: new Abstract: Long-Text Understanding (LTU) at million-token scale requires balancing reasoning fidelity with computational efficiency. Frontier long-context LLMs can

ScriptHOI: Learning Scripted State Transitions for Open-Vocabulary Human-Object Interaction Detection

arXiv:2605.05057v1 Announce Type: new Abstract: Open-vocabulary human-object interaction (HOI) detection requires recognizing interaction phrases that may not appear as annotated categories during tra

Segmenting proto-halos with vision transformers

arXiv:2508.00049v2 Announce Type: cross Abstract: The formation of dark-matter halos from small cosmological perturbations generated in the early universe is a highly non-linear process typically mode

Sentiment Analysis and Customer Satisfaction Prediction on E-Commerce Platforms Based on YouTube Comments Using the XGBoost Algorithm

arXiv:2605.04887v1 Announce Type: new Abstract: The exponential expansion of digital commerce in Indonesia has significantly shifted consumer interactions toward video-centric social networks, particu

Simultaneous CNN Approximation on Manifolds with Applications to Boundary Value Problems

arXiv:2605.04126v1 Announce Type: new Abstract: This paper develops convolutional neural network (CNN) methods for simultaneous approximation and elliptic boundary value problems on compact Riemannian

Sparsity is Combinatorial Depth: Quantifying MoE Expressivity via Tropical Geometry

arXiv:2602.03204v2 Announce Type: replace Abstract: While Mixture-of-Experts (MoE) architectures define the state-of-the-art, their theoretical success is often attributed to heuristic efficiency rath

Stop Automating Peer Review Without Rigorous Evaluation

arXiv:2605.03202v1 Announce Type: new Abstract: Large language models offer a tempting solution to address the peer review crisis. This position paper argues that today's AI systems should not be used

researcharxiv-cs-ai

Structured 3D Latents Are Surprisingly Powerful: Unleashing Generalizable Style with 2D Diffusion

arXiv:2605.04412v1 Announce Type: new Abstract: 3D asset generation plays a pivotal role in fields such as gaming and virtual reality, enabling the rapid synthesis of high-fidelity 3D objects from a s

Superposition Is Not Necessary: A Mechanistic Interpretability Analysis of Transformer Representations for Time Series Forecasting

arXiv:2605.05151v1 Announce Type: new Abstract: Transformer architectures have been widely adopted for time series forecasting, yet whether the representational mechanisms that make them powerful in N

Taming Outlier Tokens in Diffusion Transformers

arXiv:2605.05206v1 Announce Type: new Abstract: We study outlier tokens in Diffusion Transformers (DiTs) for image generation. Prior work has shown that Vision Transformers (ViTs) can produce a small

The First Token Knows: Single-Decode Confidence for Hallucination Detection

arXiv:2605.05166v1 Announce Type: new Abstract: Self-consistency detects hallucinations by generating multiple sampled answers to a question and measuring agreement, but this requires repeated decodin

The Impact of Vocabulary Overlaps on Knowledge Transfer in Multilingual Machine Translation

arXiv:2605.04196v1 Announce Type: new Abstract: Knowledge transfer, especially across related languages, has been found beneficial for multilingual neural machine translation (MNMT), but some aspects

The Implicit Curriculum: Learning Dynamics in RL with Verifiable Rewards

arXiv:2602.14872v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has been a main driver of recent breakthroughs in large reasoning models. Yet it remains a mys

The Impossibility Triangle of Long-Context Modeling

arXiv:2605.05066v1 Announce Type: new Abstract: We identify and prove a fundamental trade-off governing long-sequence models: no model can simultaneously achieve (i) per-step computation independent o

The Newsworthiness of Brazilian Distress: A Peak Analysis on Time Series of International Media Attention to Disasters in Brazil

arXiv:2605.04552v1 Announce Type: new Abstract: Media coverage influences disaster response, yet the drivers of international media attention to local events remain unevenly understood. Brazil offers

The Pinocchio Dimension: Phenomenality of Experience as the Primary Axis of LLM Psychometric Differences

arXiv:2605.05080v1 Announce Type: new Abstract: We administer 45 validated psychometric questionnaires to 50 large language models (LLMs) to identify the dimensions along which LLMs differ psychometri

The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs

arXiv:2507.14874v2 Announce Type: replace Abstract: Pattern recognition with concise and flat AND-rules makes the Tsetlin Machine (TM) both interpretable and efficient, while the power of Tsetlin auto

Topology-Preserving Data Augmentation for Ring-Type Polygon Annotations

arXiv:2603.14764v3 Announce Type: replace Abstract: Geometric data augmentation is widely used in segmentation workflows, but polygon annotations are often assumed to remain valid after transformation

Toward Human-AI Complementarity Across Diverse Tasks

arXiv:2605.04070v1 Announce Type: cross Abstract: Human-AI complementarity, the idea that combining human and AI judgments can outperform either alone, offers a promising pathway toward robust oversig

Towards Distillation-Resistant Large Language Models: An Information-Theoretic Perspective

arXiv:2602.03396v3 Announce Type: replace Abstract: Proprietary large language models (LLMs) embody substantial economic value and are generally exposed only as black-box APIs, yet adversaries can sti

Transformed Latent Variable Multi-Output Gaussian Processes

arXiv:2605.05133v1 Announce Type: new Abstract: Multi-Output Gaussian Processes (MOGPs) provide a principled probabilistic framework for modelling correlated outputs but face scalability bottlenecks w

Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity

arXiv:2605.04827v1 Announce Type: new Abstract: Label Distribution Learning (LDL) models supervision as an instance-wise probability distribution, enabling fine-grained learning under inherent ambigui